The Silent Burden: Understanding Alexithymia and Its Correlation With University Student Depression, Anxiety, and Stress in a Cross‐Sectional Study
Bibliographic record
Abstract
ABSTRACT Background and Aims In a time of increasing mental health difficulties among college students globally, where 20%–45% experience disorders annually, mood disorders emerge as a prevalent concern. These disorders mainly impact individuals aged 18 to 30 and significantly affect academic performance and long‐term well‐being. This study aimed to assess the psychological well‐being of university students, focusing on alexithymia, depression, anxiety, stress, and their complex relationships. Methods Using a multi‐stage sampling approach, the study was conducted in 2019 with 260 undergraduate students at Shahrekord University of Medical Sciences, Shahrekord, Iran. The Depression, Anxiety, and Stress Scale (DASS) questionnaire measured depression, anxiety, and stress, while the Toronto Alexithymia Scale‐20 (TAS‐20) assessed alexithymia. The data were analyzed using SPSS v.23.0. Results The participants had a mean age of 20.7 ± 3.2 years, were mostly female (75.7%), single (90.7%), and of Fars ethnicity (66.1%). The majority lived in dormitories (70.3%). Alexithymia was present in 30.8% of the population, with males scoring higher than females ( p = 0.04). Also, students aged 18–19 had lower depression ( p = 0.04) and anxiety ( p = 0.04) scores. We found significant positive correlations between alexithymia and stress, depression, and anxiety ( p < 0.001). Moreover, a strong positive correlation was observed between depression and both anxiety ( p < 0.001) and stress ( p < 0.001). Additionally, anxiety demonstrated a notable correlation with stress ( p < 0.001), underscoring the intricate interplay among these psychological factors. Conclusion Identifying alexithymia in medical settings is essential, as it can affect patient–provider communication and care. Students with alexithymic traits may benefit from interventions targeting both alexithymia and co‐occurring mental disorders like depression and anxiety. Future research should focus on developing tailored treatments and early screening to improve emotional regulation and mental health outcomes, particularly in high‐stress academic environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".